Merging weather radar observations with ground-based measurements of rainfall using an adaptive multiquadric surface fitting algorithm

A system for real-time forecasting of river flow is an essential tool in operational water management. Such systems require well calibrated hydrological and river flow models which can make use of spatially distributed and real-time rainfall observations. Weather radar products provide spatial data on rainfall. However, weather radars sense at certain altitude above ground level and are subject to a large range of error sources. Therefore, these observations often do not correspond to measurements at the ground. Through merging ground-based raingauge observations with the radar rainfall product, often referred to as data merging (or recalibration or rescaling of the radar image), one may force the radar observations to better correspond to the ground-based measurements, without losing the spatial information. In this paper, a methodology is presented to combine radar observations with ground-based measurements of precipitation in near real-time, based on an adaptive multiquadric surface fitting algorithm.